Abstract
Graph theory and the study of complex networks have, over the last decade, received increasing attention from the neuroscience research community. It allows for the description of the brain as a full network of connections, a connectome, as well as for the quantitative characterization of its topological properties. Still, there is a clear lack of standard procedures for building these networks. In this work we describe a specifically designed full workflow for the pre-processing of resting state functional Magnetic Resonance Imaging (rs-fMRI) data and connectome. The proposed workflow focuses on the removal of confound data, the minimization of resampling effects and increasing subject specificity. It is implemented using open source software and libraries through shell and python scripting, allowing its easy integration into other systems such as BrainCAT. With this work we provide the neuroscience research community with a standardized framework for the construction of functional connectomes, simplifying the interpretation and comparison of different studies.
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Magalhães, R., Marques, P., Veloso, T., Soares, J.M., Sousa, N., Alves, V. (2015). Construction of Functional Brain Connectivity Networks. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 12th International Conference. Advances in Intelligent Systems and Computing, vol 373. Springer, Cham. https://doi.org/10.1007/978-3-319-19638-1_35
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DOI: https://doi.org/10.1007/978-3-319-19638-1_35
Publisher Name: Springer, Cham
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